Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.


Introduction
Leukemia and lymphoma are the most frequent kinds of blood cancer in people of all ages, particularly young people. Tis abnormal situation is induced by red blood cell proliferation and immature growth, which can harm red blood cells, bone marrow, and the immune system [1]. Leukemia accounts for more than 3.5% of new cancer cases in the United States, with over 50,000 new cases diagnosed in 2018 [2]. Cancerous lymphoblasts in the blood travel to other organs, including the heart, brain, lungs, and arteries, before spreading to important tissues throughout the body. Red blood cells are normally in charge of transporting oxygen from the heart to all organs. Tey make up half of the total blood volume. White blood cells, on the other hand, serve an important role in the human immune system, serving as the frst line of protection against a variety of diseases and disorders [3]. As a result, accurately identifying these white blood cells is crucial in understanding the symptoms of the problem. Teir categorization is determined by their cytoplasmic composition. Changes in lymphocytes, a kind of white blood cell, cause acute lymphoblastic leukemia [4]. Acute or chronic leukemia are the two types of leukemia. Without treatment, the typical recovery period for acute myeloid leukemia is roughly three months; however, the time of appearance of chronic leukemia is longer than that of acute leukemia. Chronic lymphoblastic leukemia is the most common kind of acute leukemia, accounting for around 25% of all juvenile malignancies [5,6]. Early detection of leukemia and lymphoma has always been difcult for researchers, clinicians, and hematologists. Leukemia symptoms include enlarged lymph nodes, paleness, fever, and weight loss, although these symptoms can also be caused by other diseases [7]. Because of the moderate nature of the symptoms, diagnosing leukemia and lymphoma in its early stages is challenging. PBS microscopic assessment is the most often used leukemia and lymphoma diagnostic approach, while the gold standard for leukemia and lymphoma diagnosis only entails obtaining and analyzing white blood cell samples [8]. Several research have utilized machine learning and deep learning and machine diagnostics approaches to laboratory image processing during the last two decades in the hopes of pushing the boundaries of late diagnosis of leukemia and lymphoma and establishing their subtypes [9]. In this research, blood smear pictures were evaluated to diagnose, distinguish, and count cells in distinct kinds of leukemia and lymphoma [10].
Deep learning is a famous artifcial intelligence area that comprises algorithms and statistical associations. It has quickly permeated the feld of clinical research. Deep learning allows you to teach machines without prior skills and discover your knowledge. Te application of these technologies to medical data processing achieved outstanding results and was especially benefcial in disease detection [11]. Deep learning techniques, according to studies, signifcantly help [12] the complex medical decisionmaking processes in medical image processing [13] by extracting and then analyzing characteristics from these pictures [14]. As the number of medical diagnostic instruments increased and a considerable volume of highquality data was created, there was an urgent demand for more powerful data processing technologies. Conventional data analysis methods were incapable of analyzing such massive volumes of data or identifying data trends.
Te proposed study employs deep learning procedures driven by transfer learning and image processing to overcome the limitations of earlier investigations. Te following are the study's signifcant contributions: (i) Te proposed study used transfer learning incorporated with various algorithms for better prediction results (ii) Te proposed study used a generic approach and comparative analysis techniques have shown that the proposed study of deep learning empowered with transfer learning with image processing outperformed using the white blood cell dataset (iii) For enhanced results, the proposed study uses image processing practices (iv) Private data cloud techniques are used for data and model security (v) For performance evaluation, the proposed model used numerous performance matrices

Literature Review
Image analysis of contaminated blood cells is frequently separated into four stages: preparation, extraction, feature engineering, and classifcation. Extensive research has been conducted on numerous types of cancer, including leukemia and lymphoma. Zhang et al. proposed a convolutional neural network (CNN) model for the nonsegmented direct sorting of tissue samples into healthy and sick cells [15]. To categorize distinct kinds of white blood cells in the body, Zhao et al. ofered machine-learning approaches such as CNN, support vector machine (SVM), random forest, and others [16]. Te relative white blood cell ratio is used to determine the morphology of leukemia and lymphoma. Deep learningbased computational analysis has shown promise as a diagnostic technique for heterogeneous white blood cell count. Deep learning was demonstrated by Choi et al. [17] and Qin et al. [18] to categorize white blood cells at numerous stages of maturation, laying the foundation for a deep learningbased diagnosis of leukemia and lymphoma; however, these research fndings had constraints due to a shortage of cell types and poor sensitivity, respectively, and classifcation was usually done using precompiled images rather than raw clinical images. Te white blood cell disparity ratio for myeloid analysis is an important use of transfer learning that still requires improvement.
Karthikeyan and Poornima [19] described a novel method for segmenting and classifying acute myeloid and lymphomas that were preprocessed using histogram equalization and median fltering. Two techniques for lymphocyte segmentation were compared: clustering algorithm and k-mean. For lymphocyte segmentation, fuzzy cmeans clustering beats k-means clustering. After that, the support vector machine was utilized to separate normal and blast cells. MoradiAmin et al. [20] separated background lymphocytes using widespread pooling of C medium to increase the identifcation of acute lymphocytic leukemia cells. Following the extraction of diverse shape-based data, they used hierarchical clustering to minimize the number of parameters before providing them to assist the SVM classifer for normal and popping cell categorization.
Te authors of the study employed computer vision techniques to overcome the difculties of manual counting. In this situation, the picture has been preprocessed to remove the chance of distortion, and the proportion of white blood cells to red blood cells has been determined to determine if the image is normal or abnormal for detecting Salihah et al. [21]. Horie et al. [22] proved the diagnostic efectiveness of deep learning approaches such as CNN for esophagitis, including melanoma and adenocarcinoma, with a sensitivity of 98%.
Te authors [23] developed a CNN model for leukemia prediction that featured three key steps: CNN comparison stretching and edge extraction, followed by transfer learning depth feature extraction. In [24], authors developed a method for distinguishing tainted pictures from healthy ones that uses a convolutional neural network. Furthermore, the clustering method using the EM approach is utilized to compute the rate of infection spread thus far. Te study [25] proposed computer-aided diagnostic methods for leukemia cancer classifcation using an ensembled SVM learning approach. Te authors proposed a supervised machine learning approach to identify blood cancer and then categorise them using a fully integrated network [26].
Te study ofered classifcation models for distinguishing microscopic pictures of blood from leukemia and lymphoma patients from those who were not [27]. A pretrained CNN named AlexNet, as well as numerous additional classifers, are utilized to extract the features. In comparison to other classifers, the support vector machine fared better in tests. In the second model, AlexNet is used for extraction and classifcation only when the results demonstrate that it outperforms other models on various performance criteria.
Te authors of this study [28] presented a computational method for detecting acute leukemia and lymphoma. To begin, focusing was applied to digital microscope pictures to decrease noise and blurring. Color, form, texture, and statistics were identifed and classed as benign or malignant. Classifcation models based on K-nearest neighbors and naive Bayes were utilized. Experiments with sixty blood analyzers proved the efectiveness of the K-nearest neighbor (KNN), which had a 92.8% accuracy rate.
Te authors of this study [29] created a mechanism for categorizing acute myelogenous leukemia cells into subgroups. Initially, cells were segmented using a color k-means technique. Following that, six statistical characteristics were retrieved and fed into a multiclass SVM classifer. Te data yielded a maximum aiming accuracy of 87% and a maximum accuracy of 92.9%.
According to the study [30], a three-tier approach including extracting features, coding, and categorization is recommended. Te goal of this approach was to evaluate whether or not a patient has leukemia or lymphoma based on a picture of a sample of blood from a particular patient. A thick structurally complex transformation was used in feature extraction. At the encoding layer, the size of the recovered feature vectors was lowered. Finally, a multiclass support vector machine classifer was used to do the classifcation. Experiments with four hundred samples resulted in an accuracy of 79.38%.
Te study [31] established a classifcation system for acute myelogenous leukemia that segments grains using contour and k-signature methods. Ten, utilizing the morphology, characteristics such as cell volume, cell color, and so on were retrieved. Studies on a dataset of one hundred pictures found that the SVM classifer had up to 92% classifcation accuracy.
Te study [32] described an automated microscopic image-based technique for diagnosing leukemia and lymphoma. Te technique begins by reducing noise and blur during preprocessing. Te white blood cells were then separated using the k-means and Zack algorithms. Following that, chromatic, statistical, geometric, and textural elements were restored. Finally, to distinguish between healthy and unhealthy images, an SVM classifer was utilized. Trials on a dataset of twenty-seven pictures revealed a 93.57% classifcation accuracy.
Te research [33] created a categorization system for three forms of acute myeloid leukemia and acute multiple myeloma leukemia and acute lymphocytic leukemia. Twelve characteristics were extracted by hand from picture samples. Finally, for classifcation, a K-nearest neighbor predictor was applied. Experiments using a sample of 350 photos yielded 86% reliability. Te authors [34] developed a fvecharacteristic method for acute myelogenous leukemia categorization that improved picture contrast. An SVM classifer was used to categorize the data. Experiments with 51 photos provided a categorization accuracy of 93.5%.
To categorize acute lymphocytic leukemia and its subtypes, the authors [35] recommended using a CNN network termed a convolutional network. A dataset of 373 pictures was utilized for the evaluation, and an accuracy of 80% was reached. Experiments confrmed this method's superiority over a range of earlier techniques. Te authors [36] used numerous deep learning approaches to predict blood cancer cells including CNN and SVM and they achieved 97.04% prediction accuracy.
Previous research on the prediction of blood cancer utilizing white blood cells using machine learning and deep learning had signifcant drawbacks. Te limitations of prior investigations are shown in Table 1.
As Table 1 depicts the summary of all previous research that predicted blood cancer using machine learning, deep learning, and transfer learning techniques, every study has its own limitations. So, this study has the advantage of coping with all these limitations wisely during blood cancer prediction.  (i) Low-ratio dataset (ii) Data image processing (iii) Less number of classes Figure 1: Te proposed methodology for prediction of blood cancer using white blood cells empowered with transfer learning (this shows the research methodology of the current study, the overall process how data is fetching, training, and testing).

Materials and Methods
prediction process for blood cancer. In the frst phase, the proposed framework collects data from the hospital, preprocessed blood samples, divides all preprocessed samples into training testing sample ratios, and stores these split samples in private for easy access at any step. Te training phase imports training data samples from the private cloud and trains the AlexNet, ResNet, and MobileNet algorithms with stochastic gradient descent (SGD) with momentum, adaptive momentum estimation, and signal propagation algorithms using squares of the root. After training all algorithms of AlexNet, ResNet, and MobileNet, applying learning criteria techniques, if the learning criteria match the proposed framework expectations, then the trained model is stored separately on each algorithm's private cloud. If the proposed model does not meet the learning criteria then apply image processing techniques such as histogram equalization and again train the model and check the learning criteria.
In the third phase, choose the semi-best-trained algorithms from all private clouds and store them in another private cloud for the further testing processes. In the last phase, which is known as the testing phase, import blood samples from the cloud, import the best-performed trained model from the model secluded, and apply the testing process to predict the cancerous white blood cells. Finally, the proposed framework used numerous statistical matrices [37][38][39][40][41][42][43], e.g., classifcation accuracy (CA), negative predicted value (NPV), sensitivity, specifcity, f1-score, missclassifcation rate (MCR), positive predicted value (PPV), likelihood positive ratio (LPR), false negative rate (FNR), likelihood negative ratio (LNR), false positive rate (FPR), and Fowlkes Mallows index (FMI), all statistical matrix equations are given as follows: ∴ ζ for true class and ℵ is for the predicted class: Te descriptive algorithm of the proposed study for blood cancer prognostication utilizing transfer learningenhanced white blood cells is shown in Table 2. It represents the specifcs of the proposed framework's complete procedure.

Dataset.
In this proposed study, the dataset is acquired from the online source Ghaderzadeh et al. [43]. Te dataset consists of four classes named eosinophils, lymphocytes, monocytes, and neutrophils. Machine learning algorithms are highly data hungry [44].
Te proposed framework dataset consists of 10,000 instances, and each class instance consists of almost 2,500 blood samples. Figure 2 shows the sample data instance of each blood sample class.

Image Processing
To overcome the classifcation accuracy defciency problem, the proposed framework uses the image processing technique e-g histogram equalization to enhance the quality of blood samples. With the help of histogram equalization, as shown in Figures 3 and 4, the proposed framework enhances the contrast and intensity of pixels in blood samples. Equation

Simulation Results and Discussion
In this article, transfer learning has been used for the prediction of blood cancer empowered with eosinophils, lymphocytes, monocytes, and neutrophils incorporated into the blood cell dataset [45]. For simulation purposes to train and test the data sample, the proposed model used a Mac-Book Pro 2017, 16 giga byte random access memory, and Core i5 with a 512 Giga byte solid state drive. Te proposed framework divided the dataset into 70% and 30% blood cells for training and testing, respectively. To remove the diferent anomalies in the dataset, diferent pre-processing techniques have been implemented. Numerous transfer learning algorithms have been used to train the models and test the data samples. Diferent phases of training and testing have been discussed and elaborated on in this article. To measure the performance of all trained models and test results, the proposed framework used statistical performance parameters, and all parameters' equations are mentioned above in the methodology section. Figure 5 shows the training progress of SGD with momentum using AlexNet without image processing. To train this model, the proposed model set a learning rate of 0.001, 100 epochs, and 58 iterations per epoch. Te proposed framework of this training model has a lot of distortion and does not converge until the last epoch; all this distortion happens due to the contrast and pixels unbalancing in the data samples. So, stochastic gradient descent with momentum achieves 75.78% of CA and 24.22% MCR.  Figure 6 shows the training progress of the adaptive momentum association using AlexNet without image processing. To train this model, the proposed study set a learning rate of 0.001, 100 epochs, and 58 iterations per epoch. Te proposed framework of this training model has a lot of distortion and does not converge till the last epoch; all this distortion happens due to the contrast and pixels unbalancing in data samples. So, adaptive momentum association achieves 86.72% and 13.28% of classifcation accuracy and miss-classifcation rate, respectively. Figure 7 shows the training progress of root mean square (RMS) propagation using AlexNet without image processing. To train this model, the proposed study set a learning rate of 0.001, 100 epochs, and 58 iterations per epoch. Te Import one best trained model 11 Input pre-processed test images 12 Test analysis 13 Applying statistical performance matrix           Figure 11 shows the training progress of SGD with momentum using ResNet without image processing. To train this model, the proposed study set a learning rate of 0.001, 100 epochs, and 58 iterations per epoch. Te proposed framework of this training model has a lot of distortion and does not converge till the last epoch; all this distortion happens due to the contrast and pixels unbalancing in the data samples. So, stochastic gradient descent with momentum achieves 87.50% of classifcation accuracy and 12.50% miss-classifcation rate, respectively. Figure 12 shows the training progress of adaptive momentum association using ResNet without image processing. To train this model, the proposed study set a learning rate of 0.001, 100 epochs, and 58 iterations per epoch. Te proposed framework of this training model has a lot of distortion and does not converge till the last epoch, all this distortion happens due to the contrast and pixels unbalancing in the data samples. So, adaptive momentum association achieves 74.22% and 25.78% of classifcation accuracy and miss-classifcation rate respectively. Figure 13 shows the training progress of RMS propagation using ResNet without image processing. To train this model, the proposed study set a learning rate of 0.001, 100 epochs, and 58 iterations per epoch. Te proposed framework of this training model has a lot of distortion and does not converge till the last epoch; all this distortion happens due to the contrast and pixels unbalancing in the data samples. So, root means square propagation achieves 71.09% of classifcation accuracy and 28.91% miss-classifcation rate, respectively. Table 3 shows the training results of AlexNet models after image processing; all models tune on 5800 iterations, a 0.001 learning rate, and 100 epochs. So, the stochastic gradient descent moment outperformed the supra each training model and achieves 99.2% of classifcation accuracy and 0.8% miss-classifcation rate, respectively. Figure 14 shows the training progress of SGD moments using AlexNet after image processing. To train this model, the proposed study set a learning rate of 0.001, 100 epochs, and 58 iterations per epoch. Te proposed framework of this training model converges before 90 epochs and gives the highest training results. Table 4 shows the training results of ResNet models after image processing; all models tune on 5800 iterations, a 0.001 learning rate, and 100 epochs. So, RMSPROP outperformed all models and achieves a 69.53% and 30.47% of classifcation accuracy and miss-classifcation rate, respectively. Table 5 shows the training results of MobileNet models after image processing; all models tune on 5800 iterations, a 0.001 learning rate, and 100 epochs. So, RMSPROP outperformed all models and achieves a 73.44% and 26.56% of classifcation accuracy and miss-classifcation rate, respectively. Table 6 shows the test simulation of AlexNet models after image processing. Te proposed framework fnds that SGDM performs very well as compared with other models. SGDM predicted 719 lymphocyte cancerous cells correctly, 742 monocyte cancerous cells, and 716 neutrophil cancerous   14 Journal of Healthcare Engineering     Table 9 shows the statistical matrix results of blood cancer prediction after image processing. Tese results depict that the SGDM of AlexNet outperthiformed all models and achieves 97.3% classifcation accuracy and a 2.7% missclassifcation rate. SGDM of MobileNet performed below the performance line and achieved 60.5% classifcation accuracy and a 39.5% miss-classifcation rate. Table 10 shows the testing results of all models, and SGDM of AlexNet is outperformed as compared with other    16 Journal of Healthcare Engineering models and achieves 78.6% classifcation accuracy, 21.4% of classifcation accuracy, and miss-classifcation rate, respectively. But when the proposed framework compared these results with the results of after-image processing, the results of the proposed model after-image processing performed well as compared with these. Te proposed framework performed outstandingly as compared with the previous studies. Table 11 depicts the descriptive comparative analysis of this study with previous work, so the analysis depicts Pansombut et al. [35] achieved 80% classifcation accuracy, 20% miss-classifcation rate empowered with the CNN model on publicly available image blood samples, Madhukar et al. [34] achieved 93.5% classifcation accuracy, 6.5% missclassifcation rate empowered with an SVM classifer on publicly available image blood samples. Supardi et al. [33] achieved 86% classifcation accuracy and a 14% missclassifcation rate empowered with the KNN model on publicly available image blood samples, Mishra and Patel [32] achieved 93.57% classifcation accuracy, 6.53% missclassifcation rate empowered with SVM classifer on publicly available image blood samples. Laosai and Chamnongthai [31] achieved 92% classifcation accuracy and an 18% miss-classifcation rate empowered with the SVM model on publicly available image blood samples. Faivdullah et al. [30] achieved 79.38% classifcation accuracy, 20.72% miss-classifcation rate empowered with the SVM model on publicly available feature-based blood samples. Setiawan et al. [29] achieved 87% classifcation accuracy and 13% miss-classifcation rate empowered with SVM and K-means model on publicly available image blood samples. Kumar et al. [28] achieved 92.8% classifcation accuracy and 17.2% miss-classifcation rate empowered with CNN, Naïve Bayes,   and KNN models on publicly available image blood samples. Loey et al. [27] achieved 94.3% classifcation accuracy and 5.7% miss-classifcation rate empowered with CNN and AlexNet models on publicly available image blood samples, on the other side, the proposed study achieved the highest classifcation accuracy of 97.3% and a 2.7% of missclassifcation rate because the proposed framework used image processing techniques empowered with various transfer learning algorithms.

Conclusion and Future Work
Te early detection of blood cancer using white blood cells can help meritoriously in its cure. Te study's proposed framework consists of three transfer learning models AlexNet, MobileNet, and ResNet empowered with SGDM, ADAM, and RMSPROP. Te proposed framework applies all transfer learning models with varying learning rates efectively to white blood cancerous cells for early classifcation. To enhance the results, the proposed study used image processing techniques incorporating transfer learning and achieved the highest CA of 97.3% and 2.7% MCR. All experiments in the proposed study are comprehensively explained with respect to every model training and testing phase. Tis study helps health 5.0 to predict blood cancer in its early stages for early treatment. Furthermore, in the future, federated machine learning using the fed average technique will play a major role in better early prediction of blood cancer empowered with fuzzed machine learning and deep learning techniques will also apply more statistical techniques such as ANOVA and Chi-Square.

Abbreviations
SVM: Support vector machine CNN: Convolutional neural network KNN: K-nearest neighbor WBC: White blood cells SGD: Stochastic gradient descent CA: Classifcation accuracy NPV: Negative predicted value MCR: Miss-classifcation rate PPV: Positive predicted value LPR: Likelihood positive ratio LNR: Likelihood negative ratio FNR: False negative rate FPR: False positive rate FMI: Fowlkes mallows index ADAM: Adaptive moment estimation RMSPROP: Root mean square propagation ANOVA: Analysis of variance.

Data Availability
Te data used in this paper can be requested from the corresponding author upon request.